Detection of Single Cancer Cells in Blood with Artificially Intelligent Nanoarray

Nanomaterial-Based Strategies for Preventing Tumor Metastasis by Interrupting the Metastatic Biological Processes

Tumor metastasis is the primary cause of cancer-related deaths. The prevention of tumor metastasis has garnered notable interest and interrupting metastatic biological processes is considered a potential strategy for preventing tumor metastasis. The tumor microenvironment (TME), circulating tumor cells (CTCs), and premetastatic niche (PMN) play crucial roles in metastatic biological processes. These processes can be interrupted using nanomaterials due to their excellent physicochemical properties. However, most studies have focused on only one aspect of tumor metastasis. Here, the hypothesis that nanomaterials can be used to target metastatic biological processes and explore strategies to prevent tumor metastasis is highlighted. First, the metastatic biological processes and strategies involving nanomaterials acting on the TME, CTCs, and PMN to prevent tumor metastasis are briefly summarized. Further, the current challenges and prospects of nanomaterials in preventing tumor metastasis by interrupting metastatic biological processes are discussed. Nanomaterial-and multifunctional nanomaterial-based strategies for preventing tumor metastasis are advantageous for the long-term fight against tumor metastasis and their continued exploration will facilitate rapid progress in the prevention, diagnosis, and treatment of tumor metastasis. Novel perspectives are outlined for developing more effective strategies to prevent tumor metastasis, thereby improving the outcomes of patients with cancer.

Tunable metal-organic frameworks assist in catalyzing DNAzymes with amplification platforms for biomedical applications

Various binding modes of tunable metal organic frameworks (MOFs) and functional DNAzymes (Dzs) synergistically catalyze the emergence of abundant functional nanoplatforms. Given their serial variability in formation, structural designability, and functional controllability, Dzs@MOFs tend to be excellent building blocks for the precise "intelligent" manufacture of functional materials. To present a clear outline of this new field, this review systematically summarizes the progress of Dz integration into MOFs (MOFs@Dzs) through different methods, including various surface infiltration, pore encapsulation, covalent binding, and biomimetic mineralization methods. Atomic-level and time-resolved catalytic mechanisms for biosensing and imaging are made possible by the complex interplay of the distinct molecular structure of Dzs@MOF, conformational flexibility, and dynamic regulation of metal ions. Exploiting the precision of DNAzymes, MOFs@Dzs constructed a combined nanotherapy platform to guide intracellular drug synthesis, photodynamic therapy, catalytic therapy, and immunotherapy to enhance gene therapy in different ways, solving the problems of intracellular delivery inefficiency and insufficient supply of cofactors. MOFs@Dzs nanostructures have become excellent candidates for biosensing, bioimaging, amplification delivery, and targeted cancer gene therapy while emphasizing major advancements and seminal endeavors in the fields of biosensing (nucleic acid, protein, enzyme activity, small molecules, and cancer cells), biological imaging, and targeted cancer gene delivery and gene therapy. Overall, based on the results demonstrated to date, we discuss the challenges that the emerging MOFs@Dzs might encounter in practical future applications and briefly look forward to their bright prospects in other fields.

Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer

Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.

Recent Advances in Methods for Circulating Tumor Cell Detection

Circulating tumor cells (CTCs) are released from primary tumors and transported through the body via blood or lymphatic vessels before settling to form micrometastases under suitable conditions. Accordingly, several studies have identified CTCs as a negative prognostic factor for survival in many types of cancer. CTCs also reflect the current heterogeneity and genetic and biological state of tumors; so, their study can provide valuable insights into tumor progression, cell senescence, and cancer dormancy. Diverse methods with differing specificity, utility, costs, and sensitivity have been developed for isolating and characterizing CTCs. Additionally, novel techniques with the potential to overcome the limitations of existing ones are being developed. This primary literature review describes the current and emerging methods for enriching, detecting, isolating, and characterizing CTCs.

Modular Assembly of MXene Frameworks for Noninvasive Disease Diagnosis via Urinary Volatiles

Volatile organic compounds (VOCs) in urine are valuable biomarkers for noninvasive disease diagnosis. Herein, a facile coordination-driven modular assembly strategy is used for developing a library of gas-sensing materials based on porous MXene frameworks (MFs). Taking advantage of modules with diverse composition and tunable structure, our MFs-based library can provide more choices to satisfy gas-sensing demands. Meanwhile, the laser-induced graphene interdigital electrodes array and microchamber are laser-engraved for the assembly of a microchamber-hosted MF (MHMF) e-nose. Our MHMF e-nose possesses high-discriminative pattern recognition for simultaneous sensing and distinguishing of complex VOCs. Furthermore, with the MHMF e-nose being a plug-and-play module, a point-of-care testing (POCT) platform is modularly assembled for wireless and real-time monitoring of urinary volatiles from clinical samples. By virtue of machine learning, our POCT platform achieves noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%, providing a favorable opportunity for early disease diagnosis, disease course monitoring, and relevant research.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting

The most common intraocular pediatric malignancy, retinoblastoma (RB), accounts for ≈10% of cancer in children. Efficient monitoring can enhance living quality of patients and 5-year survival ratio of RB up to 95%. However, RB monitoring is still insufficient in regions with limited resources and the mortality may even reach over 70% in such areas. Here, an RB monitoring platform by machine learning of aqueous humor metabolic fingerprinting (AH-MF) is developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (LDI MS). The direct AH-MF of RB free of sample pre-treatment is recorded, with both high reproducibility (coefficient of variation < 10%) and sensitivity (low to 0.3 pmol) at sample volume down to 40 nL only. Further, early and advanced RB patients with area-under-the-curve over 0.9 and accuracy over 80% are differentiated, through machine learning of AH-MF. Finally, a metabolic biomarker panel of 7 metabolites through accurate MS and tandem MS (MS/MS) with pathway analysis to monitor RB is identified. This work can contribute to advanced metabolic analysis of eye diseases including but not limited to RB and screening of new potential metabolic targets toward therapeutic intervention.

Dual-Multivalent-Aptamer-Conjugated Nanoprobes for Superefficient Discerning of Single Circulating Tumor Cells in a Microfluidic Chip with Inductively Coupled Plasma Mass Spectrometry Detection

The efficient recognition of circulating tumor cells (CTCs) with an aptamer probe confers numerous benefits; however, the stability and binding affinity of aptamers are significantly hampered in real biological sample matrices. Inspired by the efficient preying mechanism by multiplex tubing feet and endoskeletons of sea urchins, we engineered a superefficient biomimetic single-CTC recognition platform by conjugating dual-multivalent-aptamers (DMAs) Sgc8 and SYL3C onto AuNPs to form a sea urchin-like nanoprobe (sea urchin-DMA-AuNPs). Aptamers Sgc8 and SYL3C selectively bind with the biomarker proteins PTK7 and EpCAM expressed on the surface of CTCs. CTCs were captured with 100% efficiency, followed by sorting on a specially designed multifunctional microfluidic configuration, integrating a single-CTC separation unit and a hydrodynamic filtrating purification unit. After sorting, background-free analysis of biomarker proteins in single CTCs was undertaken with inductively coupled plasma mass spectrometry by measuring the amount of ¹⁹⁷Au isotope in sea urchin-DMA-AuNPs. With respect to a single-aptamer nanoprobe/-interface, the dual-aptamer nanoprobe improves the binding efficiency by more than 200% (K_d < 0.35 nM). The microchip facilitates the recognition of single CTCs with a sorting separation rate of 93.6% at a flow rate of 60 μL min^-1, and it exhibits 73.8 ± 5.0% measurement efficiency for single CTCs. The present strategy ensures the manipulation and detection of a single CTC in 100 μL of whole blood within 1 h.